轻量级交叉模式信息相互加强网络用于RGB-T突出物体检测
Chengtao Lv1, Bin Wan1, Xiaofei Zhou1
1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
这项研究引入了一个用于RGB-T突出物体检测的轻量级网络,改进了移动设备的交叉模式融合. 提出的方法实现了与最先进的方法相比具有竞争力的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- RGB-T突出物体检测 (SOD) 已经进步,但现有的重型模型限制了移动应用.
- 目前的方法需要改进跨模式和跨级特征融合.
研究的目的:
- 为移动设备提供适用于RGB-T SOD的轻量级网络.
- 加强跨模式特征融合和跨层面特征融合.
主要方法:
- 为RGB-T SOD开发了一个轻量级的交叉模式信息相互增强网络.
- 在编码器和解码器中内置轻量级模块,以降低计算成本.
- 设计的交叉模式信息相互加强 (CMIMR) 和语义信息引导融合 (SIGF) 模块.
主要成果:
- 拟议的网络有效地通过整合语义和模式间信息来完善功能.
- 该SIGF模块抑制背景噪声,并提取多尺度信息,以提高检测.
- 在三个RGB-T数据集上,与15种最先进的方法相比,实现了竞争性性能.
结论:
- 轻量级网络为移动平台上的RGB-T SOD提供了有效的解决方案.
- CMIMR和SIGF模块显著提高了特征融合和突出物体检测精度.
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